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Practical Data Analysis Using Jupyter Notebook

You're reading from   Practical Data Analysis Using Jupyter Notebook Learn how to speak the language of data by extracting useful and actionable insights using Python

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781838826031
Length 322 pages
Edition 1st Edition
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Author (1):
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Marc Wintjen Marc Wintjen
Author Profile Icon Marc Wintjen
Marc Wintjen
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Data Analysis Essentials
2. Fundamentals of Data Analysis FREE CHAPTER 3. Overview of Python and Installing Jupyter Notebook 4. Getting Started with NumPy 5. Creating Your First pandas DataFrame 6. Gathering and Loading Data in Python 7. Section 2: Solutions for Data Discovery
8. Visualizing and Working with Time Series Data 9. Exploring, Cleaning, Refining, and Blending Datasets 10. Understanding Joins, Relationships, and Aggregates 11. Plotting, Visualization, and Storytelling 12. Section 3: Working with Unstructured Big Data
13. Exploring Text Data and Unstructured Data 14. Practical Sentiment Analysis 15. Bringing It All Together 16. Works Cited
17. Other Books You May Enjoy
Bringing It All Together

Welcome to the capstone chapter, where we'll walk through some examples to show you how the skills learned you've throughout this book can be applied. In this chapter, we will learn about open source real-world datasets, some tips on how to report results, and a capstone project that blends, transforms, and visualizes data from multiple sources. Data analysis is a craft and a journey rewarded by the fact that you never stop learning new ways to work with data, provide insights, and answer questions. The data literacy skills of reading, working with, analyzing, and arguing with data is agnostic to any technology, but in my experience, nothing replaces the experience of using a specific technology head down and hands-on. Our tool of choice in this book was Jupyter Notebook, along with the extendable libraries available when using the Python ecosystem, such as pandas, NumPy, and NTLK. As you continue...

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